residual block
- North America > Canada > Ontario > Toronto (0.28)
- Asia (0.04)
- North America > Canada (0.04)
- Europe > United Kingdom > England > Oxfordshire > Oxford (0.04)
- Europe > Greece > Attica > Athens (0.04)
- (2 more...)
- Research Report > Experimental Study (0.93)
- Research Report > New Finding (0.92)
- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Artificial Intelligence > Natural Language (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
- Information Technology > Software (0.67)
- North America > United States > California > San Francisco County > San Francisco (0.14)
- North America > United States > Pennsylvania > Allegheny County > Pittsburgh (0.04)
- Asia > Middle East > Jordan (0.04)
- Information Technology (0.67)
- Government > Military (0.46)
- Government > Regional Government (0.46)
How to Initialize your Network? Robust Initialization for WeightNorm & ResNets
Devansh Arpit, Víctor Campos, Yoshua Bengio
Neural Information Processing Systems http://nips.cc/
FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction
Shuyang Sun, Jiangmiao Pang, Jianping Shi, Shuai Yi, Wanli Ouyang
Besides, we observe that existing works still cannotdirectlypropagate the gradient information from deep layers to shallow layers.
661c1c090ff5831a647202397c61d73c-Paper.pdf
We take a step towards extending this result to deep ResNets. We start by two motivating examples.
- North America > United States > Massachusetts > Middlesex County > Cambridge (0.05)
- North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.04)
Residual Flows for Invertible Generative Modeling
Tian Qi Chen, Jens Behrmann, David K. Duvenaud, Joern-Henrik Jacobsen
Neural Information Processing Systems http://nips.cc/
- North America > Canada > Ontario > Toronto (0.14)
- Europe > Germany > Bremen > Bremen (0.14)
- North America > United States > Texas (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.93)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (0.48)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.30)
A Training and
All models were trained on single GPUs, except for SchNet when trained on OC20-2M, which required 3 GPUs. Tables 9-12 present the extended results on OC20 across the 4 separate S2EF validation sets. Table 9: Evaluation results on the OC20 S2EF in-distribution validation set. In Table 13, we present the performance and inference throughput of the baseline models on COLL. Table 13: Evaluation of the performance of the four baseline models on the COLL dataset.Inference COLL test set Throughput Samples / Energy MAE Force MAE Force cos EFwT Model GPU sec.
ANODEV2: A Coupled Neural ODE Framework
Tianjun Zhang, Zhewei Yao, Amir Gholami, Joseph E. Gonzalez, Kurt Keutzer, Michael W. Mahoney, George Biros
Neural Information Processing Systems http://nips.cc/
- North America > United States > Texas > Travis County > Austin (0.04)
- North America > United States > Massachusetts > Middlesex County > Cambridge (0.04)
- North America > United States > California > San Mateo County > Menlo Park (0.04)
- North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.04)